Range reexpansion after long stasis: Italian otters (Lutra lutra) at their northern edge

Abstract Species range shifts and expansion are subjects of primary research interest in the context of climate warming and biological invasions. Few studies have focused on reexpansion of species that suffered severe declines. Here, we focused on population recovery of Eurasian otters (Lutra lutra) in Italy, first detected in 2003 after a southward range contraction. We modeled the rate of range expansion and occupancy at the northern expanding front (central Italy), to gain insights into the progress of recovery and mechanisms of reexpansion. We performed a field survey in 2021, which redefined the northern limit of distribution further north, in close proximity to the Gran Sasso National Park. Then we analyzed a time series (1985–2021) of distances of northernmost occurrences from the center of the 1985 range. Using segmented regression, we were able to identify a prolonged stasis of the northern range edge and a simultaneous increase in occupancy from 0.151 to 0.4. A breakpoint was estimated in 2006, after which the range expanded northwards at an average rate of 5.48 km/year. From 2006 to 2021, the overall northward shift was about 80 km. Occupancy continued to increase until 2019 and abruptly declined in 2021. These patterns suggest that the reexpansion of the range can be limited by low occupancy at the expanding front. As occupancy increases, long‐distance dispersal increases and then range expands. The low occupancy at the current distribution limit of otters may reflect a higher anthropogenic pressure on northern habitats, which could slow down the reexpansion process.


| INTRODUC TI ON
Species range contraction and expansion driven by climate and land use changes, and biological invasions are increasingly important study areas (Novoa et al., 2020;Pacifici et al., 2020;Parmesan & Yohe, 2003;Platts et al., 2019). Accurate descriptions of range shifts are essential to predict future trends in light of climate change (Baquero et al., 2021) and develop effective control strategies of invasive species (Uden et al., 2015). However, environmental alteration and the introduction of alien species are not the only causes of species range shifts and expansion processes. Some carnivore species are expanding their ranges following severe historical contractions, due to ceased human persecution (Chapron et al., 2014;Louvrier et al., 2018;Lubina & Levin, 1988), reduction in anthropogenic pressure on habitat (Clavero et al., 2010;Marcelli et al., 2012), or mesopredator release (LaPoint et al., 2015).
The lack of long-term spatiotemporal data from the range of a species is a major obstacle for assessing range shifts. Monitoring programs collect high-quality data in the form of presence-absence (more properly, detection-non-detection) of a species, but usually on short time scales. Thus, comparing contemporary and historical surveys is often the only way to documenting range changes, especially in native species. This may come at the cost of using past data collected using non-standardized methods, often in the form of presence-only data and with low or incomplete sampling effort (Tingley & Beissinger, 2009). For these reasons, quantification of species range shifts is a challenging task, requiring the use of appropriate data and range metrics for minimizing the effects of sampling errors (Hassall & Thompson, 2010;Preuss et al., 2014;Yalcin & Leroux, 2017). In some situations, sampling effort required to assess range shifts can be reduced by restricting sampling to the range margins. Then summary statistics of the distances of species occurrences from a reference location (Hassall & Thompson, 2010;Parmesan & Yohe, 2003;Preuss et al., 2014) provide a time series of relative change in the distribution limits, which can be used to quantify range expansion or contraction over time.
Detection and quantification of range shifts, and modeling range expansion are also critical steps for assessing the progress of population recovery, adjusting future conservation efforts (Marcelli & Fusillo, 2009a;Tinker et al., 2008). A limited but growing number of studies have investigated the recolonization dynamics of apex predators that have suffered human-induced declines (Jerina & Adamič, 2008;Louvrier et al., 2018;Marcelli et al., 2012). One of the most studied cases is the range expansion of the sea otter (Enhydra lutria) across North Pacific following translocations (Tinker et al., 2008). Mechanistic models of ecological diffusion have been used to estimate spatiotemporal changes in occupancy and abundance of this gregarious carnivore from counts of individuals (Eisaguirre et al., 2021;Lubina & Levin, 1988;Williams et al., 2017Williams et al., , 2019. Here, we used presence-absence data from standard field surveys to assess the natural reexpansion of the Eurasian otter (Lutra lutra) in Italy at the northern range-edge, over a period from 1985 to 2021. The Eurasian otter is a solitary, nocturnal mesocarnivore, living mainly in rivers and feeding on fishes and anurans (Clavero et al., 2003). The species is listed in annexes II and IV of the European Habitats Directive (92/43/EEC). After a sharp decline occurred from 1950s to 1980s, many European populations have colonized their former distribution ranges, primarily as a consequence of reduced anthropogenic pressure on rivers (Clavero et al., 2010;Marcelli et al., 2012;Marcelli & Fusillo, 2009a). In Italy, the geographic range of Eurasian otters collapsed from 1970s, mainly as a result of degradation of river habitat due to economic, industrial, and infrastructure growth of Italy in the 1960s and 1970s of the last century.
Remnant populations persisted in the southern regions of the peninsula (Cassola, 1986). A comprehensive survey carried out in 2003 detected a natural recovery of the species: site occupancy increased everywhere within the borders of the former range and distribution extended toward the southernmost areas of the peninsula. A reduced impact of urban land use on river habitat was identified as a factor involved in this recovery (Marcelli & Fusillo, 2009a). Despite a significant recovery, otter populations in Italy remained confined to the southern regions of the peninsula. Accordingly, the Italian IUCN red list of vertebrate species classified the Italian population as endangered (Rondinini et al., 2013). This remnant southern population has been recognized as an evolutionary significant unit, genetically distinct from other European populations (Mucci et al., 2010). After 2003, a number of local surveys focused on range limits documented an apparent progress of the range reexpansion. While otters continued to expand southward (Marcelli & Fusillo, 2009b;unpublished data), a weak northward expansion in areas of central Italy was observed in more recent times (Lerone, 2013;Loy et al., 2015;Marcelli, M., & Fusillo, R., unpublished data). In 2019 occurrences of otters were recorded in some sites of the Pescara river, which defined the known northern limit of the distribution (Giovacchini et al., 2019).
Because of the restricted distribution, the reexpansion across the central and northern regions of the peninsula is an important piece of the conservation strategy for Italian otters (Marcelli & Fusillo, 2009a;Panzacchi et al., 2011). However, no attempt was made to compare available data and assess whether and how a northward range expansion has occurred in the last decades. Thus, the main aim of our study was to model the rate of range expansion, with a view to gaining insights into progress and potential of recovery of the otter in Italy, and mechanisms of species reexpansion. For this, we conducted an occupancy survey in 2021 and used additional data from five past surveys, to obtain a time series of the northernmost occurrences of the species. Then we used regression models in which the distance of range edge occurrences was plotted against time (year of observation) to estimate the expansion rate (Preuss et al., 2014;Wang et al., 2021).
Few studies have explicitly examined the spatiotemporal patterns of naturally reexpanding populations. Conversely, great efforts have been made to understand the processes that underlie observed patterns of biological invasions. Alien species frequently exhibit nonlinear range-versus-time curves. According to models of stratified diffusion, neighborhood dispersal increases density of populations during an initial phase of establishment with slow or no expansion (Johnson et al., 2006;Shigesada et al., 1995). Range expansion abruptly accelerates when density of donor populations reaches a threshold to provide sufficient numbers of long-distance emigrants (Johnson et al., 2006; | 3 of 12 MARCELLI et al. Shigesada et al., 1995). A similar mechanism could operate also in native species that reexpand their range (see Hurford et al., 2006 for reintroduced populations). Recovery of natural populations could start with an increase in density after the end of the range contraction process, with no extension in range until a threshold in density is reached at the expanding front. Thus, range reexpansion could proceed after a possibly long time lag imposed by dispersal limitation and the spatiotemporal pattern of driving factors (i.e., habitat recovery).
We explored this hypothesis using site occupancy as a surrogate for density (MacKenzie & Nichols, 2004). We explicitly examined whether the range expanded abruptly after a period of range stasis using segmented regression, and compared linear and nonlinear model forms to test for an early increase in occupancy and a delayed range expansion.
Our expectation is consistent with the hypothesis that low dispersal may delay colonization of distant newly suitable habitat, resulting in range limits that temporarily fall short of limits imposed by environmental gradients (Hargreaves et al., 2014;Pulliam, 2000).

| Study area
We delineated the study area based on data from several surveys conducted during the period 1985-2019 (Barrasso et al., 1992;Cassola, 1986;Giovacchini et al., 2019;Lerone, 2013;Loy et al., 2015;Marcelli & Fusillo, 2009a;Ottino, 1995), aiming to assess the current northern range margin of the Italian population of Eurasian otter. All these studies confirmed that the northern range margin was located in the Abruzzo region from the mid-1980s, with the exception of one survey that reported a more southern northern limit (Marcelli & Fusillo, 2009a). We did not consider small isolated populations in more northern regions (Latium and Tuscany) gone to extinction between 1980s and 1990s (Cassola, 1986; Reggiani, G., unpublished data) and the ongoing slow recolonization of Italian Alps from other European countries (Malthieux, 2020;Pavanello et al., 2015;Righetti, 2011;Stokel et al., 2021).
The northernmost sites of occurrences were detected in the Pescara river (central portion of Abruzzo) in 2019 (Giovacchini et al., 2019). Therefore, we included this river in our survey to delineate the southern border of the study area. The northern border was established a posteriori depending on the data that were being recorded during the survey. The resulting study area encompassed seven contiguous hydrographic basins (3696 km 2 ) in the central and northern portion of the Abruzzo Region ( Figure 1). About one-third of the area is protected by the Gran Sasso and Monti della Laga National Park.

| Field data collection
The survey was conducted during June-October 2021. We attempted to select survey sites at 5 km intervals along watercourses F I G U R E 1 Study area located in the Abruzzo Region, Italy. Survey of the Eurasian otter performed in 2021 at the northern range periphery of Italian population: Red and gray circles are survey sites where otter was detected and not detected, respectively. Blue lines are watercourses of the survey area with Strahler stream order >1 (1:250,000-scale hydrography layer). Green area displays the Gran Sasso and Monti della Laga National Park. of the study area (Strachan & Jefferies, 1996) potentially suitable for otter occupancy. Potential habitat was defined by removing stream segments with stream order 1 (sensu Strahler, 1957) from a 1:250,000 hydrographic layer clipped to the boundaries of the survey area using QGIS 3.18.2 (QGIS Development Team, 2021). For site selection purposes, we overlaid a 5 × 5 km grid across the selected hydrographic net. Within each 5 km cell we selected a stream section based on accessibility and suitable conditions for detection of otter feces (spraints). A sampling site was a stream section of 600 m in length. To confirm otter occupancy in a site, two experienced surveyors searched for spraints, inspecting both the shorelines and all emerging structures (i.e., potential marking sites) on a full transect of 600 m if they did not detect spraints within shorter distances (Reuther et al., 2000). Data collection took place during dry seasons and days without raining events to avoid the effect of spraint washing (Fusillo et al., 2007). A total of 30 different watercourses were surveyed. All survey data were georeferenced using a GPS.

| Range-expansion models
We used presence-absence data from our recent survey and five past surveys to assess a northward range expansion of Italian otters across the last four decades. Past surveys were conducted in 1985 (Cassola, 1986), 2003 (Marcelli & Fusillo, 2009a), 2012 respectively, whereas after 2003, surveys were restricted to the northern range periphery. Spatial resolution in available data differed between surveys, consisting of EEA (European Environment Agency) 10 km cells or survey points. We georeferenced data in QGIS and resampled them to a common 10 km-scale by overlapping the 10 × 10 km EEA grid with survey points. One point within each 10 km cell was randomly selected. Finally, we extracted centroids of the original cell data for spatial analysis.
We selected for each survey period a sample of occurrence sites describing the northern range margin (Preuss et al., 2014). For data from surveys of 1985 and 2003, we measured the Euclidean distance of sites of occurrence from their centroid (i.e., range center).
Then the northern sites above the 95th percentile of distances were selected as sites describing the northern range margin. Percentile To quantify the northward range expansion we developed regression models relating the distance of marginal sites from the 1985 centroid (i.e., reference range center) to the year of survey (Wang et al., 2021). The regression slope provided a measure of the annual change rate (Preuss et al., 2014;Wang et al., 2021;Ward, 2005). A range reexpansion is preceded by range contraction and may be best modeled with a non-monotonic temporal relationship. The range expansion of Italian otters at the northern margin was obvious in the recent years, but the range margin dynamics during earlier years and the onset of the expansion were not evident. We subjected data to segmented regression modeling to draw inferences about these dynamics and test for non-monotonic patterns. Segmented regression splits the relationship between response and explanatory variable into two or more generalized linear regressions to locate points where the relationship changes (Lindeløv, 2020;Muggeo, 2008).
We were interested in testing for a breaking year, where a linear increase in distance of range margin with time (i.e., range expansion) is preceded by a linear decrease or constant trend. We compared four different model forms: (1) an only-intercept model that assumed no temporal shifts in range margin (e.g., no expansion), (2) a simple linear expansion model, (3) a quadratic model to test the hypothesis of range margin contraction followed by expansion, and two onebreakpoint models, (4) a left-horizontal model (i.e., the slope of the line to the left of the breakpoint is constrained to zero) to describe a stable range margin followed by expansion, (5) a two-slope model to evaluate a variant of the hypothesis of model 3 by using two linear sections instead of an humped-shaped curve. Models 4 and 5 estimate the year where a change in slope may take place.
Statistical analyses were performed in R 4.1.2 (R Development Core Team, 2021), using the "segmented" package (Muggeo, 2022) to run segmented models. Given a regression model, this package updates it by adding one or more segmented relationships, providing slope estimates for each segment and breakpoints where slope changes. We tested for the existence of one breakpoint using the pseudo score test ("pscore.test") proposed by Muggeo (2016). We compared candidate models based on the second-order Akaike information criterion (AICc) and Akaike weights (w i ), using the AICcmodavg package (Burnham & Anderson, 2002;Mazerolle, 2020). AIC c with lower values indicate better model fit and complexity (i.e., parsimony). Akaike weights represent the weight of evidence in favor of model i being the best model of the suite of candidate models. We calculated the difference in AIC c values of alternative models relative to the model with the lowest AIC c (ΔAIC c ) to assess the relative level of support. Models with ΔAIC c ≤ 2 have substantial support, whereas models where ΔAIC c is greater have progressively less support.

| Temporal models of rangeperiphery occupancy
Tracking species distributional edges using only distance metrics may provide incomplete inference into the range expansion. We expected that an increase in local occupancy at the expansion front would precede the onset of range reexpansion, as an effect of low long-distance dispersal at low density. We predicted a delay in the northward expansion of the otter range in terms of an early increase in occupancy probability coupled with no significant changes in the range edge location. We were also interested in testing an occupancy decline in recent times as otters reexpand northwards along a putative increasing gradient of anthropogenic impact. This pattern may arise when small sinks populations (Pulliam, 2000) are maintained in suboptimal habitat by dispersal from high abundance interior populations. To explore the above hypotheses, we modeled occupancy probability of otters at the northern edge as a function of year, using presence-absence data and a Binomial distribution with a logit link. For this purpose, we extracted survey sites from minimum enclosing circles (minimum bounding geometry tool, QGIS 3.18.2) of the northernmost occurrences. These operations produced 126 presence-absence sites from range of periphery areas over years 1985-2021. We evaluated the relationship of occupancy probability with time by fitting segmented, quadratic, and linear models, using the same statistical methods as those for the distance-based analysis of range expansion.

| Field survey
We surveyed 77 sites along 30 watercourses of seven river basins and found evidence of otters in 11 sites (Table 1, Figure 1). We confirmed otter's presence in the Pescara River, first detected in 2019 (Giovacchini et al., 2019) and known as the northernmost occurrence of Italian otters. We also found otter signs in two tributaries of the Pescara river (Nora and Tirino rivers) and along the Aterno river that were not previously documented as occupied by the species.
Moreover, we detected new sites occupied by otters in the middle course of the Vomano river and in one tributary (Fiumetto stream) which redefine the current northern limit of distribution, about 45 km northwards of the Pescara river and in close proximity to the Gran Sasso and Monti della Laga National Park.

F I G U R E 2
Northernmost sites of occurrence of the Eurasian otter used to quantify the northward range expansion of the Italian population across years 1985-2021 (a-e). Range-margin occurrences were selected by five survey datasets using distances from range centers calculated for 1985 and 2003. Panel (f) shows the geographical distribution of all data of occurrence gathered from two comprehensive surveys (1985,2003).

TA B L E 1
River basins in the study area and numbers of watercourses, survey sites, and sites detected as occupied by otters.

River basin Watercourses
Survey sites

| Range-expansion models
The cumulative AICc weight for the quadratic and segmented models was 1, providing evidence that the distance of northern marginal occurrences from the reference point changed across years, but not monotonically. Although the quadratic model had the highest weight of evidence (w i = 0.587), it was only <2 AICc units higher than the left-horizontal model, indicating that these two models were equally plausible ( Table 2). Parameters estimates for the quadratic model (Table 3) predicted a slight decrease in distance from the reference point (i.e., range contraction) followed by an increase (i.e., range expansion) from the mid-1990s (Figure 3). According to the left-horizontal model (

| Temporal models of rangeperiphery occupancy
The two-slope segmented regression was the most parsimonious model for the trend in occupancy probability at the northern range edge across years (Table 4). AIC metrics showed that the relative likelihood of this model was 4.4 times higher (w i = 0.733) than the quadratic model (w i = 0.169), which ranked second (ΔAIC c = 2.9).
The remaining models received virtually no support. According

| DISCUSS ION
We modeled changes in northern range edge of Italian otters over a 36-year time period using presence-non-detection data. We de- we review the assumptions and limitations of the study and discuss the potential mechanisms underlying the inferred patterns. We conclude with implications for otter conservation and future monitoring.

| Study limitations
Although using historical data is often the only way to infer changes in species range, a problem arises from the comparability of surveys with different underlying properties (Tingley & Beissinger, 2009).
This could be a potential source of bias in our study. However, the nature of data and the methods employed should have minimized this problem. All surveys from which we obtained data used the

TA B L E 3
Parameter estimates for models of range expansion of the Eurasian otter in Italy.

F I G U R E 3
Predicted rates of range expansion and occupancy change (mean and 95% CI) at the northern range margin of the Italian population of Eurasian otter. Two regression models of distance against year were selected as equally plausible: A quadratic model (a) and a breakpoint regression model with the left slope constrained to zero (b). The best model selected for occupancy change is displayed in panel (c).
detection. This was also the case for the data used in our analysis. We are aware that our study may suffer from unmodeled detection probability. However, all data were collected through an efficient sampling protocol, involving standardized searching of otter's feces along 600 m transects, which has shown to detect the presence of otters with relative high probability (Fusillo et al., 2007;Marcelli et al., 2012), especially when experienced surveyors are involved (Jeffress et al., 2011). This requirement is largely satisfied in our dataset. Data from 2003 onwards originate from few otter experts. Observers involved in the 1985 survey had less expertise, but were specifically trained to otter surveys (Cassola, 1986). Therefore, we are confident that field methods and the intrinsic detectability of otter markings minimized potential biases from imperfect detection.
Distance measures that do not model explicitly detection probability have been traditionally used to assess species range shifts (Hickling et al., 2005;Parmesan & Yohe, 2003;Pöyry et al., 2009;Thomas & Lennon, 1999). Simulation studies have shown that the gamma quantile method for range margin description is the best choice in many sampling scenarios (Hassall & Thompson, 2010, but see Bates et al., 2015). Simple descriptive statistics based on distances of the most extreme occurrences perform well when sampling effort focuses on range margins (Hassall & Thompson, 2010;Preuss et al., 2014). Consequently, we applied this type of descriptive metric to the data. Despite some limitations, our dataset provided a rare opportunity to explore hypotheses about patterns in range expansion of a recovering species over a long time period.

| Patterns of range expansion
We were specifically interested in quantifying the northward range expansion of otters and identifying the timing of onset of this process. This required data covering a large enough temporal window to capture information prior to expansion and fitting non- The prolonged stability of the northern range margin suggests two possible scenarios. The first is that the distribution of the otter toward and beyond the northern border of the range reflected its environmental requirements. In other words, the range limit was imposed by the equilibrium of the species with the distribution of environmental suitable conditions (Sexton et al., 2009). However, for many species equilibrium conditions are unlikely, because gradients in habitat quality and quantity are always changing over time (Yackulic et al., 2015). This may be especially true when the range edge stability is protracted over long time, as in our study. Therefore, we considered a second scenario where low dispersal restricts colonization beyond range edge. We analyzed changes in site occupancy to explore this hypothesis. Our best model quantified a significant increase in occupancy during the phase of stable range margin.
Indeed, occupancy of edge populations was only 0.15 in 1985 and was predicted to increase to about 0.4 in 2006, when otters started to expand their range.
Theoretical models of species invasion explained range expansions preceded by periods of range stasis as an interaction between Allee effects (Stephens et al., 1999) and low long-distance dispersal (Johnson et al., 2006). Long-distance dispersal can produce range expansions only when edge populations have grown large enough to provide sufficient numbers of emigrants beyond the range border (Shigesada et al., 1995). If dispersal is too low to inflate the density of nascent populations above an Allee threshold (the minimum density required for a population to grow), then range expansion fails (Johnson et al., 2006).   . Models included parameters for linear, quadratic, and two-slope linear (segmented) regressions of site occupancy probability on year (binomial family, logit link function). In the left-horizontal model, the left slope was constrained to zero. Given are the relative difference in AICc (second-order Akaike information criterion) compared to the top ranked model (∆AICc), the AICc weights (w i ), the negative of twice the logarithm of the likelihood function evaluated at the maximum likelihood estimates (−2LL) and the number of parameters (np).

TA B L E 4
Model selection results for models of changes in range-periphery occupancy of the Eurasian otter in Italy.
up to about 0.4. Then the 0.4 value may have likely acted as a threshold above which increased long-distance dispersal allowed range expansion to start and progress in the later phase from 2006 onwards. We think that the environmental changes that drove range expansion in otters were also involved in the initial increase in occupancy. If this was the case, the northern range limit in the stasis phase was not in equilibrium with environmental conditions and mainly constrained by dispersal lag. An alternative to dispersal lag explanation for stable range edge is that otter population

| Conservation implications
Italian otters extended their range southward about 110 km from 1985 to 2003 (Marcelli & Fusillo, 2009a) and continued to expand in this direction in the following years (Marcelli & Fusillo, 2009b; unpublished data). Thus, range extension was smaller (about 80 km) and much more recent into northern areas than into southern areas. This may suggest a stronger resistance of northern Italian landscapes to colonization by otters due to less suitable habitat and potential barriers. Heterogeneity in landscape structure and resource availability strongly affect expansion rates across space (Fraser et al., 2015;Hastings et al., 2005;Veech et al., 2011).
Asymmetrical spatial patterns have been also observed in other native mesocarnivores (Sainsbury et al., 2019).
Although recovering otters have shown a high flexibility in their habitat selection (Weinberger et al., 2016), the strong decline in occupancy observed at the current northern range edge of Italian otters may reflect a deterioration in habitat conditions that could slow down further range reexpansion toward the northern areas.
Indeed some evidence suggests a decreasing habitat suitability from the interior areas toward the recent northern periphery of the range. The mean stream gradient (i.e., the slope of the stream's channel) of main watercourses is about two-fold higher in the current than in the former northern range periphery (south Abruzzo and Molise regions) due to mountain topography. This means that otters in the new colonized areas cope with watercourses on average steeper and more oligotrophic than in the interior range areas, with high-energy demand for moving and lower resource availability. With the exception of the Gran Sasso and Monti della Laga National Park, many rivers, especially their lower reaches with potentially high fish availability, are impacted by industrial and commercial areas, roads, water pollution, and sand-gravel extraction (Vomano and Aterno rivers, Maran, 2004;Paolini et al., 2005). In general, human population density is higher in the current than in the former northern range periphery (122 inhab/km 2 vs. 69 inhab/ km 2 ). Moreover, main watercourses are disturbed by dams and water diversion for energy production. In particular, the Vomano river and its tributaries are severely impacted by five large dams (Maran, 2004). Three of them are between 40 and 50 m high, built in narrow gorges with steep rocky walls, and therefore could be difficult to pass for otters. Therefore, although encouraging range expansion is in theory a relevant piece of the strategy for longterm conservation of Italian otters, it is a challenging task that requires active conservation and river ecosystem restoration.
Highly focused management in National Parks and in the network of riverine Special Areas of Conservation designated under the European Habitats Directive (92/43/EEC), could be a great opportunity to achieve successful conservation outcomes. Essential is the attainment of agreements between protected areas, regional governments, and hydropower companies, aimed at reducing or

| CON CLUS IONS
We first documented the presence of the otter into the Continental biogeographical region of Italy, suggesting a new monitoring task for conservation assessment under Articles 11 and 17 of the European Habitats Directive. A monitoring program can also inform habitat management aiming at encouraging the northward colonization. We strongly advocate the systematic collection of detection-non-detection data and a study-design framework for dynamic occupancy analysis (Guillera-Arroita, 2017). This involves recording repeated observations at the same sampling sites for each survey season to deal with imperfect detection of otters (Fusillo et al., 2007;Marcelli et al., 2012), allowing more accurate inference of range expansion. Our results can be used as pilot data for designing future monitoring of the northern range periphery of Italian otters. Based on our estimate of range expansion rate and a time period of 10 years, we recommend a study area extended at least 60 km in both north and south directions from the current northernmost occurrence of otters.

ACK N OWLED G M ENTS
This work was supported by the Gran Sasso and Monti della Laga National Park, Assergi (AQ), Italy (CIG Z30300A859 CUP E95D20000090001).

CO N FLI C T O F I NTE R E S T
Authors declare no conflicts of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data are available on Dryad at https://doi.org/10.5061/dryad.2280g b5ww.